ADFA

PyTorch implementation of ADFA: Attention-augmented Differentiable top-k Feature Adaptation for Unsupervised Medical Anomaly Detection

Getting Started

Install packages with:

$ pip install -r requirements.txt

Dataset

Prepare medical image as:

train data:
    dataset_path/class_name/train/good/any_filename.png
    [...]

test data:
    dataset_path/class_name/test/good/any_filename.png
    [...]

    dataset_path/class_name/test/defect_type/any_filename.png
    [...]

Download datasets

BrainMRI : Download from Kaggle website

BUSI : Download from Kaggle website

Covid19 : Download from Kaggle website

SipakMed : Download from Kaggle website

How to train

Example

python trainer.py --class_name all --data_path [/path/to/dataset/] 

Performance

Datasets	$\varepsilon$=0	$\varepsilon$=0.01	$\varepsilon$=0.05	$\varepsilon$=0.1	$\varepsilon$=0.2	pytorch top-k	randomly initialized WR50
BrainMRI	0.858	0.858	0.858	0.857	0.855	0.844	0.577
Covid	0.963	0.967	0.967	0.973	0.97	0.917	0.47
BUSI	0.958	0.959	0.962	0.966	0.965	0.952	0.591
SIPaKMeD	0.964	0.965	0.971	0.972	0.972	0.958	0.512

Reference

[1] https://github.com/sungwool/CFA_for_anomaly_localization

[2] https://github.com/xiahaifeng1995/PaDiM-Anomaly-Detection-Localization-master

[4] https://github.com/lukasruff/Deep-SVDD-PyTorch

[4] https://github.com/BangguWu/ECANet

[5] https://github.com/yerkojahve/Meta-Pseudo-label/tree/master/soft_topk